1. Field
The present disclosure relates generally to speech recognition, and more specifically to techniques for providing continuous-space phrase representations.
2. Description of Related Art
Current speech recognition systems make use of word embeddings to perform various natural language processing tasks. Relatively elementary word embeddings typically rely on 1-of-N encoding, where each word in an underlying vocabulary of size N is represented by a respective vector of dimension N. On the other hand, more sophisticated word embeddings map words of an underlying vocabulary into vectors of a lower-dimensional space, allowing for each vector to include local and/or global context of the word corresponding to the vector.
Such word embeddings, however, are not without weaknesses. For example, embeddings relying on local context poorly utilize corpus statistical information, and embeddings relying on global context present challenges when used to parse particular semantic mechanisms, such as analogies. Moreover, in many cases homonymous and/or polysemous words must be disambiguated prior to embedding for proper operation.